计算涡轮叶片结构可靠度的自适应重要抽样法

Adaptive importance sampling method for the structural reliability computation of turbine blade

关键词:  结构可靠性  取样法  涡轮叶片  可靠性分析  数值仿真

Key words:  Turbine blade  Structural reliability  Sampling method  Reliability analysis  Numerical simulation

摘要:

为直接准确地计算系统结构可靠度 ,提出了自适应重要抽样方法 ,由极限状态面失效区确定的最初抽样区开始 ,用与随机变量联合概率密度函数成比例的抽样密度 ,自适应递增调整抽样 ,使抽样区稍大于失效区 ,以减少安全区的追加抽样 ,提高抽样效率 ,并将其应用于航空发动机涡轮叶片的结构可靠性分析 ,在获得相同的准确计算结果时 ,所需计算量显著减少。

Abstract:

To compute the system structural reliability directly and accurately, the Adaptive Importance Sampling (AIS) method was presented. From an initial sampling domain constructed by the limit-state surface failure domain, using a sampling density that was proportional to the joint probability density function of the random variables, the sampling was proceeded adaptively and incrementally to let the sampling domain slightly greater than the failure domain to minimize the additional sampling in the safe domain. AIS was applied to the structural reliability analysis for aeroengine’s turbine blade. The computation was significantly reduced.